deploy-k8s-gpu-cluster

name	deploy-k8s-gpu-cluster
description	Deploy a Kubernetes GPU cluster with DeepOps (Kubespray + GPU Operator) and prove it schedules GPU pods. Use when asked to deploy or rebuild Kubernetes on GPU servers with this repository.

Deploy a Kubernetes GPU cluster

Preconditions

• Ubuntu 22.04/24.04 or RHEL/Rocky 8/9 hosts you may fully manage (driver installs may reboot them; no active users or workloads).
• SSH access from the provisioning machine to every host as a sudo-capable user.
• submodules/kubespray initialized — Kubernetes playbooks fail on missing kubespray_defaults role imports without it.
• Run everything from the repository root.

Procedure

1. Prepare the environment and verify it:

   git submodule update --init --recursive
   ./scripts/setup.sh
   cp -r config.example config
   

2. Edit config/inventory: control plane nodes under [kube_control_plane] and [etcd], workers under [kube_node] (a single machine can hold all three roles).

3. Preflight — must pass before deploying:

   python3 scripts/validation/deepops_doctor.py --remote --json
   

4. Deploy:

   ansible-playbook -l k8s_cluster playbooks/k8s-cluster.yml
   

   This runs Kubespray and installs the NVIDIA GPU Operator. Expect roughly 45–90 minutes on a first run.

5. Validate — the success signal is this, not the play recap:

   python3 scripts/validation/validate_k8s.py --json --cuda-smoke
   

   Require "ok": true with nodes_ready == nodes_total, gpus_allocatable > 0, and cuda_smoke_ok: true.

Failure branches

• Playbook fails on a transient error: rerun the same playbook; Kubespray is rerun-safe. A converged rerun reports changed=0.
• Syntax/import error mentioning kubespray_defaults: the submodule is not initialized; run git submodule update --init --recursive.
• gpus_allocatable: 0: the GPU Operator stack is not ready. Check kubectl get pods -A | grep -i nvidia — the driver DaemonSet can take 10+ minutes on first deploy; if pods are crash-looping, follow skills/diagnose-driver-install/.
• CUDA smoke pod stuck Pending: kubectl -n deepops-validate describe pod deepops-validate-cuda — usually no allocatable GPU (see above) or an image pull problem on airgapped networks (use --cuda-image to point at a mirrored image).
• Single-node clusters: control plane taints are handled by the playbook for the single-node case; if pods stay Pending on a multi-role node, check taints with kubectl describe node <name> | grep -i taint.